In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Deep learning models can take weeks to train on a single GPU-equipped machine,
necessitating scaling out DL training to a GPU-cluster. However, current distributed DL …

Convolutional neural networks (CNN) have led to a successful breakthrough for
hyperspectral image classification (HSIC). Due to the intrinsic spatial-spectral specificities of …

To achieve automatic recommendation of privacy settings for image sharing, a new tool
called iPrivacy (image privacy) is developed for releasing the burden from users on setting …

Deep learning can be applied in various business fields for better performance. Especially,
fashion-related businesses have started to apply deep learning techniques on their e …

There is growing interest in improving the design of deep network architectures to be both
accurate and low cost. This paper explores semantic specialization as a mechanism for …

Fitting model parameters to a set of noisy data points is a common problem in computer
vision. In this work, we fit the 6D camera pose to a set of noisy correspondences between …

Abstract Systems and methods may automatically generate code for deep learning
networks. The systems methods may provide a code generation framework for generating …

We propose a novel deep neural network that is both lightweight and effectively structured
for model parallelization. Our network, which we name as SplitNet, automatically learns to …